A Python framework for studying quantum chaos and information scrambling in many-body quantum systems, with focus on the Sachdev-Ye-Kitaev (SYK) model and driven Hubbard model.
This framework provides tools for:
- Constructing SYK and Hubbard model Hamiltonians via Jordan-Wigner transformation
- Computing spectral statistics for quantum chaos diagnostics
- Calculating Out-of-Time-Ordered Correlators (OTOC) for scrambling analysis
- Simulating Floquet dynamics and quasi-energy spectra
- Quantum kernel methods for machine learning
- SYK Model: Random all-to-all 4-body Majorana interactions
- Driven Hubbard Model: 1D lattice with time-periodic hopping
- Level spacing ratio analysis (GOE/GUE/Poisson classification)
- Fermion parity sector projection
- Disorder averaging with error estimation
- Finite-size scaling extrapolation
- OTOC calculation for scrambling timescales
- Trotter-Suzuki decomposition for time evolution
- Floquet operator and quasi-energy computation
- Depolarizing, amplitude damping, and phase damping channels
- Realistic noise modeling for near-term quantum devices
git clone https://github.com/yourusername/quantum-chaos-framework.git
cd quantum-chaos-framework
pip install -r requirements.txt- Python >= 3.8
- NumPy >= 1.20
- SciPy >= 1.7
- Matplotlib >= 3.4
- PennyLane >= 0.30
- scikit-learn >= 1.0
from hamiltonians.syk_hamiltonian import SYKHamiltonian, disorder_average_lsr
# Create SYK model
syk = SYKHamiltonian(n_majorana=8, coupling_strength=1.0, seed=42)
# Get chaos diagnostics
print(f"Ground state energy: {syk.eigenvalues[0]:.4f}")
print(f"Level spacing ratio: {syk.get_level_spacing_ratio():.4f}")
print(f"Level spacing (even parity): {syk.get_level_spacing_ratio_parity('even'):.4f}")
# Disorder averaging for statistics
stats = disorder_average_lsr(n_majorana=8, n_realizations=20)
print(f"⟨r⟩ = {stats['mean']:.4f} ± {stats['stderr']:.4f}")quantum_chaos_framework/
├── hamiltonians/
│ ├── syk_hamiltonian.py # SYK model with disorder averaging
│ └── hubbard_hamiltonian.py # Driven Hubbard model with Floquet
├── circuits/
│ ├── otoc_calculator.py # OTOC computation methods
│ └── trotter_evolution.py # Quantum circuit simulation
├── noise/
│ ├── noise_channels.py # Quantum noise models
│ └── nisq_simulator.py # NISQ device simulation
├── qml/
│ ├── quantum_kernel.py # Quantum kernel methods
│ └── quantum_classifier.py # Variational classifiers
├── utils/
│ ├── jordan_wigner.py # Fermion-to-qubit mapping
│ └── helpers.py # Utility functions
├── visualization/
│ └── quantum_visualizer.py # Plotting tools
├── main.py # Example script
└── requirements.txt
The Sachdev-Ye-Kitaev model describes N Majorana fermions with random all-to-all 4-body interactions:
Key properties:
- Maximally chaotic (saturates chaos bound λ ≤ 2πT/ℏ)
- Holographic duality to AdS₂ gravity
- Solvable in large-N limit
Level spacing ratio for random matrix classification:
- GOE (chaotic): ⟨r⟩ ≈ 0.530
- GUE (complex chaotic): ⟨r⟩ ≈ 0.603
- Poisson (integrable): ⟨r⟩ ≈ 0.386
Out-of-Time-Ordered Correlators measure information scrambling:
Exponential decay indicates quantum chaos with Lyapunov exponent λ.
- Sachdev, S. & Ye, J. (1993). Gapless spin-fluid ground state in a random quantum Heisenberg magnet. Phys. Rev. Lett.
- Kitaev, A. (2015). A simple model of quantum holography. KITP talks.
- Maldacena, J. & Stanford, D. (2016). Remarks on the SYK model. Phys. Rev. D.
- Maldacena, J., Shenker, S. & Stanford, D. (2016). A bound on chaos. JHEP.
MIT License - see LICENSE file for details.
Contributions welcome! Please submit issues and pull requests.
Quantum Computing Researcher
